Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Kód výsledku v IS VaVaI

RIV/47813059:19240/16:#0005528 - isvavai.cz

Výsledek na webu

http://dx.doi.org/10.1155/2016/3460293

DOI - Digital Object Identifier

10.1155/2016/3460293

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Popis výsledku v původním jazyce

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in decision-making process of financial managers. The work focuses mainly on one representative of the intelligent soft computing techniques ? artificial neural networks. Authors suggest the new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm and a moving average. The moving average is supposes to to enhance the outputs of the network using the error part of the original RBF. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day.To determine the forecasting efficiency, they perform the comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimiz

Název v anglickém jazyce

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Popis výsledku anglicky

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in decision-making process of financial managers. The work focuses mainly on one representative of the intelligent soft computing techniques ? artificial neural networks. Authors suggest the new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm and a moving average. The moving average is supposes to to enhance the outputs of the network using the error part of the original RBF. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day.To determine the forecasting efficiency, they perform the comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimiz

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

ED1.1.00/02.0070: Centrum excelence IT4Innovations

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

The Scientific World Journal

ISSN

2356-6140

e-ISSN

—

Svazek periodika

—

Číslo periodika v rámci svazku

2016

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—